## [1] "Loading the following libraries using lb_myRequiredPackages: data.table"
## [2] "Loading the following libraries using lb_myRequiredPackages: lubridate"
## [3] "Loading the following libraries using lb_myRequiredPackages: ggplot2"
## [4] "Loading the following libraries using lb_myRequiredPackages: readr"
## [5] "Loading the following libraries using lb_myRequiredPackages: plotly"
## [6] "Loading the following libraries using lb_myRequiredPackages: knitr"To extract and visualise tweets and re-tweets of #dockercon for 17 - 21 April, 2017 (DockerCon17).
Borrowing extensively from http://thinktostart.com/twitter-authentification-with-r/
Data should have been already downloaded using collectData.R. This produces a data table with the following variables (after some processing):
## [1] "text" "favorited" "favoriteCount"
## [4] "replyToSN" "created" "truncated"
## [7] "replyToSID" "id" "replyToUID"
## [10] "statusSource" "screenName" "retweetCount"
## [13] "isRetweet" "retweeted" "longitude"
## [16] "latitude" "location" "language"
## [19] "profileImageURL" "createdLocal" "obsDateTimeMins"
## [22] "obsDateTimeHours" "obsMin" "obsHour"
## [25] "obsQH" "obsQHour" "obsDateTime5m"
## [28] "obsDateTime10m" "obsDateTime15m"The table has 6,912 tweets (and 8,714 re-tweets) from 5,294 tweeters between 2017-04-16 19:01:03 and 2017-04-19 15:38:54 (Central District Time).
All (re)tweets containing #dockercon 2017-04-17 to 2017-04-19 FALSE = tweets, TRUE = re-tweets
We want to make a nice map but sadly we see that most tweets have no lat/long set.
| latitude | longitude | nTweets |
|---|---|---|
| NA | NA | 15581 |
| 30.26250 | -97.74010 | 25 |
| 30.26037 | -97.73848 | 2 |
| 30.25820 | -97.71264 | 1 |
| 30.25888 | -97.73841 | 2 |
| 30.25971 | -97.73940 | 1 |
| 30.26006 | -97.73813 | 1 |
| 30.26006 | -97.73859 | 1 |
| 30.26720 | -97.76390 | 2 |
| 30.26036 | -97.73848 | 1 |
| 30.26356 | -97.73993 | 1 |
| 30.26416 | -97.73961 | 2 |
| 30.26623 | -97.74328 | 1 |
| 30.26857 | -97.73617 | 1 |
| 30.26471 | -97.74174 | 1 |
| 30.20227 | -97.66723 | 1 |
| 42.36488 | -71.02168 | 1 |
| 37.61698 | -122.38428 | 1 |
This appears to be pulled from the user’s profile although it may also be a ‘guestimate’ of current location.
Top locations for tweets:
| location | nTweets |
|---|---|
| NA | 2310 |
| San Francisco, CA | 1162 |
| San Francisco | 451 |
| Austin, TX | 283 |
| Seattle, WA | 199 |
| Silicon Valley, CA | 188 |
| Paris | 144 |
| Islamabad, Pakistan | 137 |
| London | 119 |
| New York, NY | 118 |
| Charlotte, NC | 113 |
| USA | 103 |
| west tokyo | 103 |
| San Jose, CA | 98 |
| Boulder, CO | 97 |
Top locations for tweeters:
| location | nTweeters |
|---|---|
| NA | 966 |
| San Francisco, CA | 162 |
| Austin, TX | 76 |
| San Francisco | 61 |
| Seattle, WA | 44 |
| San Jose, CA | 40 |
| New York, NY | 40 |
| Paris | 35 |
| London, England | 29 |
| New York | 27 |
| Paris, France | 27 |
| London | 25 |
| Palo Alto, CA | 25 |
| Dallas, TX | 23 |
| Washington, DC | 23 |
Next we’ll try by screen name.
Top tweeters:
| screenName | nTweets |
|---|---|
| DockerCon | 300 |
| theCUBE | 154 |
| BettyJunod | 119 |
| climbingkujira | 117 |
| jpetazzo | 108 |
| solomonstre | 104 |
| jeanepaul | 103 |
| ManoMarks | 82 |
| OpenShiftNinja | 82 |
| sitspak | 81 |
| SFoskett | 78 |
| vmblog | 77 |
| jameskobielus | 77 |
| kaslinfields | 74 |
| bsmith626 | 73 |
And here’s a really bad visualisation of all of them!